Open rahulnarayaniitk opened 4 months ago
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Hello @rahulnarayaniitk I am a GsSOC'24 contributor and I also would like to work on this issue. Can you please assign it to me?
Hello @Niketkumardheeryan sir I'm GSSoC24 contributors . Can you please assign me this issue
Hello @Niketkumardheeryan sir, I hope you having a great time. I’m a GSSoC’24 contributor and I had like to contribute to this issue can you please assign it to me ?
Thank You
Hello @rahulnarayaniitk I am a GsSOC'24 contributor and I also would like to work on this issue. Can you please assign it to me?
I am sorry I am a contributor too.
I am interested in this issue. Please assign me this
Hey @Niketkumardheeryan, I would like to raise an issue in order to contribute to GSSoC '24. I am proposing to make a project named Disease Prediction using ML. AIM: to implement a robust machine-learning model that can efficiently predict the disease of a human, based on the symptoms that he/she possesses. APPROACH: Gathering the Data: Data preparation is the primary step for any machine learning problem. We will be using a dataset from Kaggle for this problem. This dataset consists of two CSV files one for training and one for testing. Cleaning the Data: Cleaning is the most important step in a machine learning project. The quality of our data determines the quality of our machine-learning model. So it is always necessary to clean the data before feeding it to the model for training. Model Building: After gathering and cleaning the data, the data is ready and can be used to train a machine learning model. We will be using this cleaned data to train the Support Vector Classifier, Naive Bayes Classifier, and Random Forest Classifier. We will be using a [confusion matrix]to determine the quality of the models. Inference: After training the three models we will be predicting the disease for the input symptoms by combining the predictions of all three models. This makes our overall prediction more robust and accurate.
Kindly assign this to me under GSSoC '24 and provide a suitable level to it accordingly.